Neglecting structural breaks when estimating and valuing dynamic correlations for asset allocation
研究了在资产配置中忽略动态相关性模型的结构断裂会导致参数估计偏差，蒙特卡洛模拟显示短期和长期持久性分别被低估和高估，影响投资组合分散化、对冲和风险管理决策。
This paper assesses the econometric and economic value consequences of neglecting structural breaks in dynamic correlation models and in the context of asset allocation framework. It is shown that changes in the parameters of the conditional correlation process can lead to biased estimates of persistence. Monte Carlo simulations reveal that short-run persistence is downward biased while long-run persistence is severely upward biased, leading to spurious high persistence of shocks to conditional correlation. An application to stock returns supports these results and concludes that neglecting such structural shifts could lead to misleading decisions on portfolio diversification, hedging, and risk management.